Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose (original) (raw)
Related papers
Bird Diversity Modeling Using Geostatistics and ArcGIS
Portugal has a diverse landscape that provides a variety of habitats for birds. Many species of birds that breed in Portugal migrate south in the autumn and return in the spring. Portugal also serves as a wintering ground for several northern species. Since migrating bird species sometimes cover long distances and several habitats, they can serve as indicators of the overall health of the environment. This study uses geostatistical methods and ArcGIS to construct predictive bird diversity models and extract habitat information from the European Environmental Agency’s CORINE Land Cover Classification. This will result in habitat preferences based on the predictions. The combination of diversity modeling and habitat characterization can be used to aid conservation efforts in identifying key habitats of importance. The chosen interpolators to produce the predictive surfaces are Inverse Distance Weighting and Ordinary Kriging. The study will also conduct comparative analysis of the interpolators and an assessment of their accuracy.
Global Ecology and Conservation, 2021
Changes in distribution and abundance of species affect the entirety of biodiversity and monitoring these changes is critical for the efficient conservation of integrity and functions of species population. However, acquiring accurate information on biodiversity over large spatial scales poses a challenge since such data is patchy and incomplete, if not unavailable, in many areas. This study aims at examining the applicability of a novel approach based on Species Distribution Models (SDMs) to develop spatial predictions of Essential Biodiversity variables (EBVs; variables to be quantified at certain points in time and space to monitor variations in biodiversity) for birds based on bird diversity metrics such as the distributions of properties of key bird habitats. A major objective of this study is to build bird SDMs which can be used to derive spatial EBVs for bird species at a regional scale. We used as predictors 16 environmental variables that are known to be ecologically meaningful for birds, including two bioclimatic variables (Bio17 = precipitation of driest quarter and Bio7 = temperature annual range) for three periods of 'current', 'future 2050′, and 'future 2070′, eleven landcover (land use) predictors, the normalized difference vegetation index, and two topographic variables (slope and topography). We used multiple modeling techniques to build presenceonly SDMs relating bird presence to environmental features of each species. Here, we show that the suitability estimated according to the SDMs can be used as a spatial 'species distribution' EBV (SD EBV) and reflect the habitat quality and trends in land use and climatic impacts on populations of bird species. These developments could facilitate monitoring of bird species across space and time, ultimately helping to identify priority conservation areas, estimate habitat suitability and provide early warning signs regarding bird distribution trends. In general, bioclimatic variables, topography and forest structure were identified to have important ties to the species probability maps generated on the basis of the SDMs, signifying a dominant role of bioclimatic variable Bio17 in the development of habitat suitability patterns.
Global Ecology and Biogeography, 2008
Aim To evaluate geostatistical approaches, namely kriging, co-kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km 2 basin. Location Taiwan. Methods Kriging, co-kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co-kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co-kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro-scale and the macro-scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro-scale. Co-kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small-scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co-kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.
Diversity and Distributions, 2007
Mapping of species distributions at large spatial scales has been often based on the representation of gathered observations in a general grid atlas framework. More recently, subsampling and subsequent interpolation or habitat spatial modelling techniques have been incorporated in these projects to allow more detailed species mapping. Here, we explore the usefulness of data from long-term monitoring (LTM) projects, primarily aimed at estimating trends in species abundance and collected at shorter time intervals (usually yearly) than atlas data, to develop predictive habitat models. We modelled habitat occupancy for 99 species using a bird LTM program and evaluated the predictive accuracy of these models using independent data from a contemporary and comprehensive breeding bird atlas project from the same region. Habitat models from LTM data using generalized linear modelling were significant for all the species and generally showed a high predictive power, albeit lower than that from atlas models. Sample size and species range size and niche breadth were the most important factors behind variability in model predictive accuracy, whereas the spatial distribution of sampling units at a given sample size had minor effects. Although predictive accuracy of habitat modelling was strongly species dependent, increases in sample size and, secondarily, a better spatial distribution of sampling units should lead to more powerful predictive distribution models. We suggest that data from LTM programs, now established in a large number of countries, has the potential for being a major source of good quality data suitable for the estimation and regularly update of distributions at large spatial scales for a number of species.
Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns
ISPRS International Journal of Geo-Information, 2015
In this paper we explore relationships between bird species richness and environmental factors in New York State, focusing particularly on how spatial scale, autocorrelation and nonstationarity affect these relationships. We used spatial statistics, Getis-Ord Gi*(d), to investigate how spatial scale affects the measurement of richness "hot-spots" and "cold-spots" (clusters of high and low species richness, respectively) and geographically weighted regression (GWR) to explore scale dependencies and nonstationarity in the relationships between richness and environmental variables such as climate and plant productivity. Finally, we introduce a geovisualization approach to show how these relationships are affected by spatial scale in order to understand the complex spatial patterns of species richness.
Ecological Modelling, 2004
Predictive habitat models rely on the relationship between a response variable (either occurrence or abundance of a species) and a set of environmental predictors. Vegetation is generally preferred as a source of potential predictors because of having a more direct link with reproductive necessities of species than topography and climate. However, vegetation maps are costly to produce and update and most land-use/land-cover maps are usually made with a general purpose, focused on land management, and not thinking on animal distribution. On the contrary, basic topographic and climatic maps are easier to obtain or to derive, are not so sensitive to legend design, and do not need of such frequent updates. In this study, we compare the predictive ability of different sets of environmental predictors when modelling the distribution of breeding birds. Models were generated for 79 bird species in South-western Spain using Generalised Additive Models (GAMs) with binomial errors and logit link. For each species, several models were created that differed in the set of candidate predictors initially tested (either derived from vegetation or from topo-climatic maps) or in the conditional order in which those predictor sets were tested. Within vegetation predictors, a similar strategy was used to ascertain the relative relevance of vegetation landscape (variables describing the surrounding habitat matrix) compared to vegetation cover (variables describing the type of vegetation found on sampling sites).
Predicting Bird Habitat Quality From a Geospatial Analysis of FIA Data
The ability to assess the influence of site-scale forest structure on avian habitat suitability at an ecoregional scale remains a major methodological constraint to effective biological planning for forest land birds in North America. We evaluated the feasibility of using forest inventory and analysis (FIA) data to define vegetation structure within forest patches, which were delineated from independent geospatial data sets of ecological subsection, forest type, and landform class. We used Swainson's warbler (Limnothlypis swainsonii, Audubon) as a model to demonstrate how to integrate FIA data with geospatial data sets to estimate and monitor habitat suitability for a priority bird species in the West Gulf
Geostatistical techniques for predicting bird species occurrences
2011
Habitat loss and fragmentation are major threats to biodiversity. Geostatistical methods, especially kriging, are widely used in ecology. Bird counts data often fail to show normal distribution over an area which is required for most of the kriging methods. Hence ...